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ballen4705 authored
for the values. I may add some additional values as time goes on, so this is prelimiary. The SMART status check now uses the correct ATA call. If failure is detected we search through attributes to list the failed ones. If the SMART status check shows GOOD, we then look to see if their are any usage attributes or prefail attributes have failed at any time. If so we print them. Modified function that prints vendor attributes to say if the attribute has currently failed or has ever failed. git-svn-id: https://smartmontools.svn.sourceforge.net/svnroot/smartmontools/trunk@86 4ea69e1a-61f1-4043-bf83-b5c94c648137
ballen4705 authoredfor the values. I may add some additional values as time goes on, so this is prelimiary. The SMART status check now uses the correct ATA call. If failure is detected we search through attributes to list the failed ones. If the SMART status check shows GOOD, we then look to see if their are any usage attributes or prefail attributes have failed at any time. If so we print them. Modified function that prints vendor attributes to say if the attribute has currently failed or has ever failed. git-svn-id: https://smartmontools.svn.sourceforge.net/svnroot/smartmontools/trunk@86 4ea69e1a-61f1-4043-bf83-b5c94c648137
grid_F0F1F2.py 1.68 KiB
import pyfstat
import numpy as np
import matplotlib.pyplot as plt
try:
from gridcorner import gridcorner
except ImportError:
raise ImportError(
"Python module 'gridcorner' not found, please install from "
"https://gitlab.aei.uni-hannover.de/GregAshton/gridcorner")
F0 = 30.0
F1 = 1e-10
F2 = 0
Alpha = 1.0
Delta = 1.5
# Properties of the GW data
sqrtSX = 1e-23
tstart = 1000000000
duration = 10*86400
tend = tstart+duration
tref = .5*(tstart+tend)
depth = 20
label = 'grid_F0F1F2'
outdir = 'data'
h0 = sqrtSX / depth
data = pyfstat.Writer(
label=label, outdir=outdir, tref=tref,
tstart=tstart, F0=F0, F1=F1, F2=F2, duration=duration, Alpha=Alpha,
Delta=Delta, h0=h0, sqrtSX=sqrtSX)
data.make_data()
m = 0.01
dF0 = np.sqrt(12*m)/(np.pi*duration)
dF1 = np.sqrt(180*m)/(np.pi*duration**2)
dF2 = 1e-17
N = 100
DeltaF0 = N*dF0
DeltaF1 = N*dF1
DeltaF2 = N*dF2
F0s = [F0-DeltaF0/2., F0+DeltaF0/2., dF0]
F1s = [F1-DeltaF1/2., F1+DeltaF1/2., dF1]
F2s = [F2-DeltaF2/2., F2+DeltaF2/2., dF2]
Alphas = [Alpha]
Deltas = [Delta]
search = pyfstat.GridSearch(
'grid_F0F1F2', 'data', data.sftfilepath, F0s, F1s,
F2s, Alphas, Deltas, tref, tstart, tend)
search.run()
F0_vals = np.unique(search.data[:, 2]) - F0
F1_vals = np.unique(search.data[:, 3]) - F1
F2_vals = np.unique(search.data[:, 4]) - F2
twoF = search.data[:, -1].reshape((len(F0_vals), len(F1_vals), len(F2_vals)))
xyz = [F0_vals, F1_vals, F2_vals]
labels = ['$f - f_0$', '$\dot{f} - \dot{f}_0$', '$\ddot{f} - \ddot{f}_0$',
'$\widetilde{2\mathcal{F}}$']
fig, axes = gridcorner(
twoF, xyz, projection='log_mean', labels=labels, whspace=0.1, factor=1.8)
fig.savefig('{}/{}_projection_matrix.png'.format(outdir, label))